Machine translation (MT) is experiencing a renaissance. On one hand, machine translation is becoming more common and used in ever larger scale, on the other hand many translators have an almost hostile attitude towards machine translation programs and those translators who use MT as a tool. Either it is assumed that the MT can never be as good as a human translation or machine translation is viewed as the ultimate enemy of the translator and as a job killer. The article discusses with various examples the limits and possibilities of machine translation. It demonstrates that machine translation can be better than human translations – even if they were made by experienced professional translators. The paper also reports the results of a test that showed that translation customers must expect that even well-known and expensive translation service providers deliver a quality that is on par with poor MT. Overall, it is argued that machine translation programs are no more and no less than an additional tool with which the translation industry can satisfy certain requirements. This abstract was also – as the entire article – automatically translated into English.

Machine translation (MT) is experiencing a renaissance. On one hand, machine translation is becoming more common and used in ever larger scale, on the other hand many translators have an almost hostile attitude towards machine translation programs and those translators who use MT as a tool. Either it is assumed that the MT can never be as good as a human translation or machine translation is viewed as the ultimate enemy of the translator and as a job killer. The article discusses with various examples the limits and possibilities of machine translation. It demonstrates that machine translation can be better than human translations – even if they were made by experienced professional translators. The paper also reports the results of a test that showed that translation customers must expect that even well-known and expensive translation service providers deliver a quality that is on par with poor MT. Overall, it is argued that machine translation programs are no more and no less than an additional tool with which the translation industry can satisfy certain requirements. This abstract was also – as the entire article – automatically translated into English.

Automatic translations, or machine translations, get more and more advanced and common. This paper aims to examine how well Google Traduction works for translating an ideological text. To what extent can a computer program interpret such a text, and render the meaning of complex thoughts and ideas into another language ? In order to study this, UNESCOS World Report Investing in Cultural Diversity and Intercultural Dialogue has been translated from french to swedish, first automatically and then manually. Focusing on denotations, connotations, grammar and style, the two versions have been analysed and compared. The conclusion drawn is that while Google Traduction impresses by its speed and possibilites, editing the automatically translated text in order to correctly transmit the mening and the message of the text to the target language reader, would probably be a more time-consuming process than writing a direct translation manually.

Google Translate’s mission is “to enable everyone, everywhere, to understand the world and express themselves across languages” (Pitman, 2021). But are algorithms capable of leading us beyond the translation of the word toward an understanding of the world? Computational linguistics research has been interested in assessing this kind of real-world effects of technology and invited other disciplines to join their effort. With this purpose, this study examines the ways the algorithmic agency (Maly, 2022) elicits a ‘movement of meanings’ (Silverstone, 1999) when mediating news reports from English to Portuguese – the official language of Brazil, the country with the greatest use of Google Translate (Turovsky, 2016). For that, it investigates how algorithms convert appraisal and semiotic elements that carry ideological stances. The bilingual sample consists of six news articles on the U.S. Capitol attack published in U.S. outlets, two each of right, center, and left political leaning, along with their translations obtained through Google Translate. The analytical framework encompasses Fairclough’s (2003) CDA methods that allow an exploration of how discourses embedded in these texts represent the social phenomena that are being depicted. This lens is complemented by the Appraisal theory (Martin & White, 2005) to investigate how value positions are constructed within texts through evaluation. A third analytical tool is necessary to engage with the ways in which meanings are moved from source to target texts. For this, van Leeuwen’s (2008) notion of recontextualization affords an assessment of the processes inherent to translations. The analysis showed that algorithms neutralized appraisal through lexical choices, changed semiotic elements through recontextualization, and blurred stances by standardizing the target language. The paper, thus, concludes that Google Translate constructed power by renaming reality and enacted it by reshaping evaluations, advancing research that seeks to examine algorithms’ impacts on digital discourse. Speaking from the epistemic locus of the Global South, this thesis proposes a critical reflection on the ideologies concealed by the self-proclaimed discourse of the universality of digital technologies.

<p>Many multilingual learners who use a language other than English at home face academic challenges from English monolingualism prevalent in the U.S. school system. English as a New Language (ENL) programs teach English to these learners while playing a role in reinforcing English monolingualism. For educational inclusivity and equity for multilingual learners, it is imperative to center their holistic language repertoires in ENL classrooms; however, this can be challenging due to individual and contextual factors. Using translanguaging as a conceptual framework, this qualitative case study explores how high school multilingual learners’ languages are flexibly used in ENL classes and how the students think about such classroom translanguaging practices. I used ethnographic methods to observe ENL classroom activities and instructional practices, interview the participants, and collect photos and documents in a high school in Indiana for a semester. The participants were an English-Spanish proficient ENL teacher and four students from Puerto Rico, Mexico, Honduras, and the Democratic Republic of the Congo, whose language repertoires included Spanish, Lingala, French, Arabic, and English. The findings describe the difficulties and possibilities of incorporating all students’ multilingual-multisemiotic repertoires in ENL classes. The classroom language practices primarily constituted of Spanish and drawing; some instructional activities and practices, such as the multigenre identity project and the teacher’s use of Google Translate, well integrated the students’ multilingual-multisemiotic repertoires. When the students engaged in English writing, they frequently used machine translation, such as Google Translate, through dynamic processes involving evaluation. While the students perceived such classroom translanguaging practices generally positively, they considered using machine translation as a problem, a resource, or an opportunity. With these findings, I argue that multilingual learners’ competence to use their own languages and machine translation technology freely and flexibly is a valuable resource for learning and should be encouraged and developed in ENL classrooms. To do so, ENL teachers should use instructional activities and practices considering students’ dynamic multilingualism. TESOL teacher education should develop such competence in teachers, and more multilingual resources should be provided to teachers. In the case of a multilingual classroom with singleton students, building mutual understanding, empathy, and equity-mindedness among class members should be prioritized. Finally, I recommend that the evolving multilingual technologies, such as machine translation, be actively used as teaching and learning resources for multilingual learners.</p>

This research investigated the feasibility of South African translations of Dewey Decimal Classification (DDC). The study provides an introductory overview of DDC throughout the world, followed by its use in South Africa. The introduction highlights shortcomings and possible solutions – of which translations seem to be the most ideal. This research involved a critical analysis of the literature on DDC translations, a documentary analysis and technology-based research in the form of Google translations and evaluation of parts of Abridged Edition 15 of DDC. The critical analysis of the literature and the documentary analysis identified problems relating to translations, how translations deal with shortcomings in DDC, the fact that no literature exists on multilingual translations, and the process of translations (including the fact that this is an expensive endeavour). It also revealed information about sponsorship and the mixed translation model. The technology-based research, using Google Translate for translations of parts of Abridged Edition 15 and the subsequent evaluation of these translations indicated that Google translations were comprehensive and needed minimum editorial effort. Further to this it paved the way for describing a possible workflow for South African translations and indicated that the parts already translated as well as further Google translations can expedite the translation process. A model for South African translations, based on only the cost of the Pansoft translation software was proposed. The mixed model approach, where some languages are used as main languages (schedules, Relative Index terms and the like) and others for Relative Index terms only, was deemed the most appropriate in the South African context. This led to the conclusion that DDC translations into ten of the official South African languages are indeed feasible. The research supports translations that keep the integrity of DDC intact, with possible expansions based on literary arrant. It is important, though, to get the support of the South African library community and authoritative bodies such as the National Library of South Africa and/or the Library and Information Association of South Africa (LIASA) to negotiate and sign a contract for these translations. / Information Science / D. Litt. et Phil. (Information Science)

This diploma thesis deals with the quality of the output of publicly available machine translation engines: Amazon Translate, Bing Translator, Deep L and Google Translate. The aim of the thesis was to qualitatively compare each machine translation engine and to test their capabilities on several types of texts - expressive, informative and operative. The analysis is carried out on translations of German texts into Czech. The thesis consists of two parts, in the theoretical part we discuss the origin and development of machine translation, its types, the automatic machine translation engines which are the object of our research, and we present a two-stage model for evaluating the quality of translation. In the empirical part, the results of the analysis based on the models of K. Reiß and A. Torrens are presented. These results show that the listed machine translation engines can be ranked from the highest to the lowest level of output quality as follows: Deep L, Google Translate, Bing Translator and Amazon Translate. Furthermore, it also turns out that the error rate correlates with the creativity of the text.

Работа посвящена сравнению качества перевода технического текста с английского языка на русский, полученного с помощью различных систем машинного перевода (СМП), на примере научной статьи металлургической тематики “Fluid Dynamics Studies of Bottom-blown and Side-blown Copper Smelting Furnaces”. В первой части работы рассматриваются общие вопросы машинного и технического перевода: приводятся понятия машинного перевода и технического перевода, определяются основные способы и алгоритмы работы СМП, выделяются основные особенности и проблемы технического перевода. Особое внимание уделяется выявлению функциональных возможностей наиболее популярных сервисов машинного перевода, к которым относятся такие СМП как Google Translate, Яндекс Переводчик, Bing Microsoft Translator, SYSTRAN Translate, PROMT.One. Во второй части работы раскрываются особенности металлургической терминологии и специфика ее заимствования, разрабатывается методика оценки качества перевода технического текста металлургической направленности, проводится апробационное исследование переводов, выполненных с помощью различных систем машинного перевода и определяются перспективы использования СМП в металлургической отрасли. Большое внимание уделяется оценке качества переводов по представленным методикам, при этом все результаты приводятся в виде таблиц с указанием количества ошибок и итоговых баллов. Результаты исследования могут быть использованы на различных предприятиях металлургического сектора при работе с документацией на иностранных языках. / This paper is devoted to comparison of translation quality obtained as a result of using machine translation (MT) systems to translate technical documents from English into Russian in the case of a metallurgical article “Fluid Dynamics Studies of Bottom-blown and Side-blown Copper Smelting Furnaces”. The first part of the paper covers general issues of machine and technical translation, including concepts of machine and technical translation, the main operation methods and algorithms, and the main features of technical translation. Particular attention is paid to identification of core functionality of the most popular machine translation systems, which include Google Translate, Yandex Translate, Bing Microsoft Translator, SYSTRAN Translate, PROMT.One. The second part of the paper reveals the main features of metallurgical terminology and the specifics of its naturalization. It also contains several translation quality assessment methodologies and includes an analysis of machine translation quality. Moreover, it features the main prospects for using MT systems in metallurgy. Much attention is paid to translation quality assessment on methodologies presented in the paper. All the results are presented in the form of tables with the number of errors and final scores for each MT system. The results of the study may be used at various metallurgical enterprises working with documentation in foreign languages.